Estimated Phytoplankton Size#

../../../_images/2a56ad505fbc95993745c26f6c45fec343eaea74223d4e09a0da4b779ffa99b5.png

Figure. Estimated Median Phytoplankton Size from satellite. The map (top) shows the change in the estimated median phytoplankton size (μm equivalent spherical diameter, ESD) in the vicinity of Palau over the period 1998-2023 derived from satellite remotely sensed sea surface temperature and ocean color data. The grey line is the Palau EEZ. The line plot (bottom) shows the change in the estimated median phytoplankton size (μm ESD) averaged over the area within the top plot. The black line represents the trend, which is not statistically significant (p > 0.05).

Hide code cell source
import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys

import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from myst_nb import glue 

sys.path.append("../../../../indicators_setup")

from ind_setup.plotting_int import plot_timeseries_interactive, plot_oni_index_th
from ind_setup.plotting import plot_base_map, plot_map_subplots, add_oni_cat, plot_bar_probs, fontsize

sys.path.append("../../../functions")
from data_downloaders import download_ERDDAP_data, download_oni_index

Setup#

Define area of interest

#Area of interest
lon_range  = [129.4088, 137.0541]
lat_range = [1.5214, 11.6587]

EEZ shapefile

shp_f = op.join(os.getcwd(), '..', '..','..', 'data/Palau_EEZ/pw_eez_pol_april2022.shp')
shp_eez = gpd.read_file(shp_f)

Download Data#

DATASET: https://oceanwatch.pifsc.noaa.gov/erddap/info/md50_exp/index.html

update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"
Hide code cell source
base_url = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/md50_exp.csv'
dataset_id = 'MD50'

if update_data:
    date_ini = '1998-01-01T00:00:00Z'
    date_end = '2024-12-01T00:00:00Z'
    data = download_ERDDAP_data(base_url, dataset_id, date_ini, date_end, lon_range, lat_range)
    data_xr = data.set_index(['latitude', 'longitude', 'time']).to_xarray()
    data_xr['time'] = pd.to_datetime(data_xr.time)
    data_xr = data_xr.coarsen(longitude=2, latitude=2, boundary = 'pad').mean()
    data_xr.to_netcdf(op.join(path_data, f'griddap_{dataset_id}.nc'))
else:
    data_xr = xr.open_dataset(op.join(path_data, f'griddap_{dataset_id}.nc'))

Analysis#

Plotting#

Average#

fig, ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data_xr.longitude, data_xr.latitude, data_xr.mean(dim='time')[dataset_id], transform=ccrs.PlateCarree(), 
                cmap = 'YlGnBu', vmin = 0.8, vmax = 1.3)
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label='Phytoplankton size MD50 (µm ESD)')
glue("average_map", fig, display=False)
plt.savefig(op.join(path_figs, 'F16_phytoplankton_mean_map.png'), dpi=300, bbox_inches='tight')
../../../_images/2a56ad505fbc95993745c26f6c45fec343eaea74223d4e09a0da4b779ffa99b5.png

Annual average#

data_y = data_xr.resample(time='1YE').mean()
fig = plot_map_subplots(data_y, 'MD50', shp_eez = shp_eez, cmap = 'YlGnBu', vmin = 0.4, vmax = 1.6, cbar = 1)
../../../_images/551b4dcd958c6a9f3aa5fb00f538a369d55d8386eb33c2db6980942442af05a0.png

Annual anomaly#

data_an = data_y - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.3, vmax=.3, cbar = 1)
../../../_images/c5b9bb8b900e65798431e87ab461a8ee10d0847a14c0021a2d57e4c7ba7177ec.png

Average over area#

dict_plot = [{'data' : data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : 'Median Phytoplankton Size - MEAN AREA'},]
fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12), 
                                 label_yaxes = 'Phytoplankton MD50 (µm ESD)');
fig.write_html(op.join(path_figs, 'F16_phytoplankton_mean_trend.html'), include_plotlyjs="cdn")

Timeseries at a given point#

loc = [7.35, 134.48]
dict_plot = [{'data' : data_xr.sel(longitude=loc[1], latitude=loc[0], method='nearest').to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : f'Median Phytoplankton Size at [{loc[0]}, {loc[1]}]'},]
fig, ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
ax.plot(loc[1], loc[0], '*', markersize = 12, color = 'royalblue', transform=ccrs.PlateCarree(), label = 'Location Analysis')
ax.legend()
<matplotlib.legend.Legend at 0x186951b80>
../../../_images/9fcf55ad7d1a6ee4e160ea4b836a3952f8ac1ef3c807c402f455b68163f18752.png
fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12),
                                 label_yaxes = 'Phytoplankton MD50 (µm ESD)');

ONI index analysis#

if update_data:
    p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
    df1 = download_oni_index(p_data)
    df1.to_pickle(op.join(path_data, 'oni_index.pkl'))
else:
    df1 = pd.read_pickle(op.join(path_data, 'oni_index.pkl'))
lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)

Group by ONI category

df1 = add_oni_cat(df1, lims = lims)
df1['ONI'] = df1['oni_cat']
data_xr['ONI'] = (('time'), df1.iloc[np.intersect1d(data_xr.time, df1.index, return_indices=True)[2]].ONI.values)
data_xr['ONI_cat'] = (('time'), np.where(data_xr.ONI < lims[0], -1, np.where(data_xr.ONI > lims[1], 1, 0)))
data_oni = data_xr.groupby('ONI_cat').mean()

Average#

fig = plot_map_subplots(data_oni, dataset_id, shp_eez = shp_eez, cmap = 'YlGnBu', vmin = 0.8, vmax = 1.3, 
                  sub_plot= [1, 3], figsize = (20, 9),  cbar = True, cbar_pad = 0.1,
                  titles = ['La Niña', 'Neutral', 'El Niño'],)
plt.savefig(op.join(path_figs, 'F16_phytoplankton_ENSO.png'), dpi=300, bbox_inches='tight')
../../../_images/2d77314fa7241414cfbc5c302d18810b1d9ea683385cd22fdc0eea1eaa2a5b3f.png

Anomaly#

data_an = data_oni - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.1, vmax=.1,
                  sub_plot= [1, 3], figsize = (20, 9),  cbar = True, cbar_pad = 0.1,
                  titles = ['La Niña', 'Neutral', 'El Niño'],)
../../../_images/365051cd9dbdfba051c5c97ebe5c76b546451636857021d3e6f6046d35b5820c.png